“As a successor of LSTM. We have a new thing. It’s not published, it’s hidden. It’s called XLSTM,” says the German computer scientist Sepp Hochreiter
The approach can be used to train networks with over 6 million neurons
With the back-and-forth praising and acknowledgement of each other’s work since ChatGPT’s launch, Karpathy’s jump to OpenAI was long due.
What makes this smart search feature exciting is the effortlessness in allowing users to search directly through prompts, eliminating the fuss of keywords
Understanding the language of animals and communicating with them is one of the longest-running fields of study in technology and biological sciences alike.
Sonnet creates high-level networks that are easier to train and test with multiple applications.
CovidDeep harnesses the grow-and-prune DNN synthesis paradigm to improve accuracy.
DM21 uses a neural network to approximate the energy function component of Density Functional Theory.
The theorem prover achieved 41.2% vs 29.3% on the miniF2F benchmark, a challenging collection of high-school olympiad problems.
Learning rate is an important parameter in neural networks for that we often spend much time tuning it and we even don’t get the optimum result even trying for some different rates.
A team of software engineers at Facebook, led by Software Engineer Bertrand Maher, recently released a JIT compiler for CPUs based on LLVM, called NNC, for “Neural Network Compiler.”
Non-deep networks could be utilised to create low-latency recognition systems, rather than deep networks.